Academic Journal
Developing A Neural Network-Based Model for Identifying Medicinal Plant Leaves Using Image Recognition Techniques.
| Τίτλος: | Developing A Neural Network-Based Model for Identifying Medicinal Plant Leaves Using Image Recognition Techniques. |
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| Συγγραφείς: | Tiwari, Nidhi, Gupta, Bineet Kumar, Prakash, Abhijityaditya, Tiwari, Kartikesh, Alshmrany, Sami, Ali, Arshad, Husain, Mohammad, Singh, Devendra |
| Πηγή: | Journal of Advanced Zoology; 2023 Supplement, Vol. 44, p1944-1958, 15p |
| Θεματικοί όροι: | Image recognition (Computer vision), Pattern recognition systems, Artificial neural networks, Digital image processing, Neural computers, Plant identification, Medical marijuana, Medicinal plants |
| Περίληψη: | Herbal plants contribute an important role in people's health and the environment, as they can provide both medical benefits and oxygen. Many herbal plants contain valuable therapeutic elements that can be passed down to future generations. Traditional methods of identifying plant species, such as manual measurement and examination of characteristics, are labor-intensive and time-consuming. To address this, there has been a push to develop more efficient methods using technology, such as digital image processing and pattern recognition techniques. The exact recognition of plants uses methodologies like computer vision and neural networks, which have been proposed earlier. This approach involves neural network models such as CNN, ALexnet, and ResNet for identifying the medical plants based on their respective features. Classification metrics give the 96.82 average accuracies. These results have been promising, and further research will involve using a larger dataset and going more into deep-learning neural networks to improve the accuracy of medicinal plant identification. It is hoped that a web or mobile-based system for automatic plant identification can help increase knowledge about medicinal plants, improve techniques for species recognition, and participate in the preservation of species that are considered ad endangered. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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